An adaptive threshold filter for ultrasound signal rejection

Tsui, Po-Hsiang; Chang, Chien‐Cheng; Chang, Chien-Chung; Huang, Norden E.; Ho, Ming‐Chih

doi:10.1016/j.ultras.2008.10.007

Cited by 18 publications

(21 citation statements)

References 10 publications

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“…The quantitative analysis of the signals showed that the C 1 and C 2 account for up to 90% of the amplitude of the backscattered signals, as shown in Figure 5. This finding agreed well with the previous study [14], supporting the selection of C 1 and C 2 for the definition of the IER. Moreover, the results in Figure 4 further showed that the summation of each IMF multiplied by the gain factor equals the signal multiplied using the same gain, demonstrating the reasonability of Equations (12) and (13).…”

Section: Experiments On a Tissue-mimicking Phantomsupporting

confidence: 93%

“…Notably, the EMD decomposed image data to produce the IMFs from high-to low-frequency components. The first IMF was the data with the highest-frequency components compared with the other IMFs; therefore, the signal waveforms in the C1 image were similar to those of the original backscattered RF echoes but more symmetrical in the upper and lower envelopes [14,17]. This also explains why the other IMF-based images (C2, C3 and C4 images in Figure6) exhibited poorer spatial resolutions compared with the C1 image.…”

Section: Experiments On a Tissue-mimicking Phantommentioning

confidence: 93%

“…In particular, the amplitude of the third IMF starts to be smaller than 10% of that of the original backscattered signals [14], and thus the IMFs from C 3 to C n may not be the primary components of the signals. For the above reasons, an IMF-based echogenicity ratio (IER) was defined using the first and second IMFs of the backscattered signals for signal normalization:…”

Section: Theoretical Demonstrationmentioning

confidence: 99%

“…In this condition, two arbitrary IMFs of the backscattered signal can be further used for normalization. For ultrasound signals, higher-frequency IMFs have larger signal amplitudes and relevant physical meanings [14].…”

Section: Theoretical Demonstrationmentioning

confidence: 99%

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Empirical Mode Decomposition of Ultrasound Imagingfor Gain-Independent Measurement on Tissue Echogenicity: A Feasibility Study

Zhou

et al. 2017

Applied Sciences

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Empirical mode decomposition (EMD) is an adaptive method for decomposing a signal into intrinsic mode functions (IMFs).This study explored using EMD of ultrasound imaging for gain-independent measurements on tissue echogenicity. The IMF-based echogenicity ratio (IER) was proposed using the first (C 1 ) and second IMFs (C 2 ) of ultrasound radiofrequency data. Experiments on lipid phantoms were conducted to investigate the practical performance of IER. Phantoms with lipid concentrations 0%-30% (n = 36) were scanned using a clinical ultrasound scanner to acquire the radiofrequency data under different gains (12-33 dB) for EMD and IER calculations. Experiments on a tissue-mimicking phantom were further performed using the same ultrasound system and data acquisition procedure to investigate the effect of ultrasound frequency on the IER at5-8 MHz.Experimental results showed that the IER measured under 33-dB gain decreased from 6.65 ± 0.23 to 3.97 ± 0.10 when the lipid concentrations were increased from 0% to 30%. When 12-dB gain was used, the IER decreased from 6.21 ± 0.29 to 3.39 ± 0.07. However, whenincreasing the frequency, the IER had a mean decreasing rate of −8.67% per MHz, which was lower than those of the C 1 and C 2 intensities.The proposed IER may allow gain-independent measurement on tissue echogenicity.

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Section: Experiments On a Tissue-mimicking Phantomsupporting

confidence: 93%

Section: Experiments On a Tissue-mimicking Phantommentioning

confidence: 93%

Section: Theoretical Demonstrationmentioning

confidence: 99%

Section: Theoretical Demonstrationmentioning

confidence: 99%

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Empirical Mode Decomposition of Ultrasound Imagingfor Gain-Independent Measurement on Tissue Echogenicity: A Feasibility Study

Zhou

et al. 2017

Applied Sciences

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“…Re cently, we de vel oped an adap tive thresh old fil ter based on a noise-as sisted em pir i cal mode de com po si tion (EMD) of ul tra sonic ech oes to re ject small sig nals in B-scans with out chang ing the char ac ter is tics of the pre served back scat tered data. 18 Our pi lot stud ies showed that the adap tive thresh old fil ter might have the abil ity to im prove the sen si tiv ity of the Nakagami pa ram e ter to de tect the vari a tion in scat terer con cen tra tions with a tol er a ble Nakagami pa ram e ter over es ti ma tion. The EMD is the key part of the Hilbert-Huang transform (HHT), pro posed as an adap tive time-fre quency anal y sis method for non lin ear and nonstationary data.…”

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confidence: 96%

Effect of Adaptive Threshold Filtering on Ultrasonic Nakagami Parameter to Detect Variation in Scatterer Concentration

et al. 2010

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The Nakagami parameter is associated with the Nakagami distribution estimated from ultrasonic backscattered signals and closely reflects the scatterer concentrations in tissues. There is an interest in exploring the possibility of enhancing the ability of the Nakagami parameter to characterize tissues. In this paper, we explore the effect of adaptive thresholdfiltering based on the noise-assisted empirical mode decomposition of the ultrasonic backscattered signals on the Nakagami parameter as a function of scatterer concentration for improving the Nakagami parameter performance. We carried out phantom experiments using 5 MHz focused and nonfocused transducers. Before filtering, the dynamic ranges of the Nakagami parameter, estimated using focused and nonfocused transducers between the scatterer concentrations of 2 and 32 scatterers/mm3, were 0.44 and 0.1, respectively. After filtering, the dynamic ranges of the Nakagami parameter, using the focused and nonfocused transducers, were 0.71 and 0.79, respectively. The experimental results showed that the adaptive threshold filter makes the Nakagami parameter measured by a focused transducer more sensitive to the variation in the scatterer concentration. The proposed method also endows the Nakagami parameter measured by a nonfocused transducer with the ability to differentiate various scatterer concentrations. However, the Nakagami parameters estimated by focused and nonfocused transducers after adaptive threshold filtering have different physical meanings: the former represents the statistics of signals backscattered from unresolvable scatterers while the latter is associated with stronger resolvable scatterers or local inhomogeneity due to scatterer aggregation.

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A novel approach to speckle noise filtering based on Artificial Bee Colony algorithm: An ultrasound image application

Latifoğlu

2013

Computer Methods and Programs in Biomedicine

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An adaptive threshold filter for ultrasound signal rejection

Cited by 18 publications

References 10 publications

Empirical Mode Decomposition of Ultrasound Imagingfor Gain-Independent Measurement on Tissue Echogenicity: A Feasibility Study

Empirical Mode Decomposition of Ultrasound Imagingfor Gain-Independent Measurement on Tissue Echogenicity: A Feasibility Study

Effect of Adaptive Threshold Filtering on Ultrasonic Nakagami Parameter to Detect Variation in Scatterer Concentration

A novel approach to speckle noise filtering based on Artificial Bee Colony algorithm: An ultrasound image application

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